Segmentation Driven Object Detection with Fisher Vectors

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2013-01-01

Author

Cinbiş, Ramazan Gökberk
Schmid, Cordelia

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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We present an object detection system based on the Fisher vector (FV) image representation computed over SIFT and color descriptors. For computational and storage efficiency, we use a recent segmentation-based method to generate class-independent object detection hypotheses, in combination with data compression techniques. Our main contribution is a method to produce tentative object segmentation masks to suppress background clutter in the features. Re-weighting the local image features based on these masks is shown to improve object detection significantly. We also exploit contextual features in the form of a full-image FV descriptor, and an inter-category rescoring mechanism. Our experiments on the PASCAL VOC 2007 and 2010 datasets show that our detector improves over the current state-of-the-art detection results.

Subject Keywords

Detectors, Object detection, Image color analysis, Training , Vectors, Feature extraction, Image segmentation

URI

https://hdl.handle.net/11511/56837

DOI

https://doi.org/10.1109/iccv.2013.369

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Department of Computer Engineering, Conference / Seminar

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Citation Formats

R. G. Cinbiş and C. Schmid, “Segmentation Driven Object Detection with Fisher Vectors,” 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/56837.